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<title>Doxygen: pcl::DecisionForestTrainer&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt; 模板类 参考</title>
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<div class="header">
  <div class="summary">
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1_decision_forest_trainer-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::DecisionForestTrainer&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt; 模板类 参考</div>  </div>
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<p>Trainer for decision trees.  
 <a href="classpcl_1_1_decision_forest_trainer.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="decision__forest__trainer_8h_source.html">decision_forest_trainer.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:acc7032e3f6878b366becf5e5199ffbfa"><td class="memItemLeft" align="right" valign="top"><a id="acc7032e3f6878b366becf5e5199ffbfa"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#acc7032e3f6878b366becf5e5199ffbfa">DecisionForestTrainer</a> ()</td></tr>
<tr class="memdesc:acc7032e3f6878b366becf5e5199ffbfa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor. <br /></td></tr>
<tr class="separator:acc7032e3f6878b366becf5e5199ffbfa"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a593a78b1a9ef4730ce8d53240f62b594"><td class="memItemLeft" align="right" valign="top"><a id="a593a78b1a9ef4730ce8d53240f62b594"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a593a78b1a9ef4730ce8d53240f62b594">~DecisionForestTrainer</a> ()</td></tr>
<tr class="memdesc:a593a78b1a9ef4730ce8d53240f62b594"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
<tr class="separator:a593a78b1a9ef4730ce8d53240f62b594"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a694f64f2fc578961105cb2de053c8ebe"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a694f64f2fc578961105cb2de053c8ebe">setNumberOfTreesToTrain</a> (const size_t num_of_trees)</td></tr>
<tr class="memdesc:a694f64f2fc578961105cb2de053c8ebe"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the number of trees to train.  <a href="classpcl_1_1_decision_forest_trainer.html#a694f64f2fc578961105cb2de053c8ebe">更多...</a><br /></td></tr>
<tr class="separator:a694f64f2fc578961105cb2de053c8ebe"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:affb20f4c78f522abf7a6e318ca999958"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#affb20f4c78f522abf7a6e318ca999958">setFeatureHandler</a> (<a class="el" href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a>&lt; FeatureType, DataSet, ExampleIndex &gt; &amp;feature_handler)</td></tr>
<tr class="memdesc:affb20f4c78f522abf7a6e318ca999958"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the feature handler used to create and evaluate features.  <a href="classpcl_1_1_decision_forest_trainer.html#affb20f4c78f522abf7a6e318ca999958">更多...</a><br /></td></tr>
<tr class="separator:affb20f4c78f522abf7a6e318ca999958"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2d91edee4cd00765989daa0bb138dc16"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a2d91edee4cd00765989daa0bb138dc16">setStatsEstimator</a> (<a class="el" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt; LabelType, NodeType, DataSet, ExampleIndex &gt; &amp;stats_estimator)</td></tr>
<tr class="memdesc:a2d91edee4cd00765989daa0bb138dc16"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the object for estimating the statistics for tree nodes.  <a href="classpcl_1_1_decision_forest_trainer.html#a2d91edee4cd00765989daa0bb138dc16">更多...</a><br /></td></tr>
<tr class="separator:a2d91edee4cd00765989daa0bb138dc16"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac9fb156d93023ae28d2868cc77f9f232"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#ac9fb156d93023ae28d2868cc77f9f232">setMaxTreeDepth</a> (const size_t max_tree_depth)</td></tr>
<tr class="memdesc:ac9fb156d93023ae28d2868cc77f9f232"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the maximum depth of the learned tree.  <a href="classpcl_1_1_decision_forest_trainer.html#ac9fb156d93023ae28d2868cc77f9f232">更多...</a><br /></td></tr>
<tr class="separator:ac9fb156d93023ae28d2868cc77f9f232"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af157ebf3ab0fc36d970a56df889d0764"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#af157ebf3ab0fc36d970a56df889d0764">setNumOfFeatures</a> (const size_t num_of_features)</td></tr>
<tr class="memdesc:af157ebf3ab0fc36d970a56df889d0764"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the number of features used to find optimal decision features.  <a href="classpcl_1_1_decision_forest_trainer.html#af157ebf3ab0fc36d970a56df889d0764">更多...</a><br /></td></tr>
<tr class="separator:af157ebf3ab0fc36d970a56df889d0764"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaefbd227253039f1762c023363866977"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#aaefbd227253039f1762c023363866977">setNumOfThresholds</a> (const size_t num_of_threshold)</td></tr>
<tr class="memdesc:aaefbd227253039f1762c023363866977"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses.  <a href="classpcl_1_1_decision_forest_trainer.html#aaefbd227253039f1762c023363866977">更多...</a><br /></td></tr>
<tr class="separator:aaefbd227253039f1762c023363866977"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a51bb668cde26ec215c738d6ea49321e0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a51bb668cde26ec215c738d6ea49321e0">setTrainingDataSet</a> (DataSet &amp;data_set)</td></tr>
<tr class="memdesc:a51bb668cde26ec215c738d6ea49321e0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the input data set used for training.  <a href="classpcl_1_1_decision_forest_trainer.html#a51bb668cde26ec215c738d6ea49321e0">更多...</a><br /></td></tr>
<tr class="separator:a51bb668cde26ec215c738d6ea49321e0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab3e0194e7f6c31d26903d963c26bcbd0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#ab3e0194e7f6c31d26903d963c26bcbd0">setExamples</a> (std::vector&lt; ExampleIndex &gt; &amp;examples)</td></tr>
<tr class="memdesc:ab3e0194e7f6c31d26903d963c26bcbd0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Example indices that specify the data used for training.  <a href="classpcl_1_1_decision_forest_trainer.html#ab3e0194e7f6c31d26903d963c26bcbd0">更多...</a><br /></td></tr>
<tr class="separator:ab3e0194e7f6c31d26903d963c26bcbd0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af73b24f6125f364b9002da7f31d272b7"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#af73b24f6125f364b9002da7f31d272b7">setLabelData</a> (std::vector&lt; LabelType &gt; &amp;label_data)</td></tr>
<tr class="memdesc:af73b24f6125f364b9002da7f31d272b7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the label data corresponding to the example data.  <a href="classpcl_1_1_decision_forest_trainer.html#af73b24f6125f364b9002da7f31d272b7">更多...</a><br /></td></tr>
<tr class="separator:af73b24f6125f364b9002da7f31d272b7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abe3c4a349ff797233b7588b144072380"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#abe3c4a349ff797233b7588b144072380">setMinExamplesForSplit</a> (size_t n)</td></tr>
<tr class="memdesc:abe3c4a349ff797233b7588b144072380"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the minimum number of examples to continue growing a tree.  <a href="classpcl_1_1_decision_forest_trainer.html#abe3c4a349ff797233b7588b144072380">更多...</a><br /></td></tr>
<tr class="separator:abe3c4a349ff797233b7588b144072380"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a525a99f17519b3b66f022e4fb6522381"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a525a99f17519b3b66f022e4fb6522381">setThresholds</a> (std::vector&lt; float &gt; &amp;thres)</td></tr>
<tr class="memdesc:a525a99f17519b3b66f022e4fb6522381"><td class="mdescLeft">&#160;</td><td class="mdescRight">Specify the thresholds to be used when evaluating features.  <a href="classpcl_1_1_decision_forest_trainer.html#a525a99f17519b3b66f022e4fb6522381">更多...</a><br /></td></tr>
<tr class="separator:a525a99f17519b3b66f022e4fb6522381"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5576065b2e1de3fc70a03822665437c4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a5576065b2e1de3fc70a03822665437c4">setDecisionTreeDataProvider</a> (boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_decision_tree_trainer_data_provider.html">pcl::DecisionTreeTrainerDataProvider</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt; &gt; &amp;dtdp)</td></tr>
<tr class="memdesc:a5576065b2e1de3fc70a03822665437c4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Specify the data provider.  <a href="classpcl_1_1_decision_forest_trainer.html#a5576065b2e1de3fc70a03822665437c4">更多...</a><br /></td></tr>
<tr class="separator:a5576065b2e1de3fc70a03822665437c4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa277da116e3e9eab9f6e95b11257b7d4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#aa277da116e3e9eab9f6e95b11257b7d4">setRandomFeaturesAtSplitNode</a> (bool b)</td></tr>
<tr class="memdesc:aa277da116e3e9eab9f6e95b11257b7d4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Specify if the features are randomly generated at each split node.  <a href="classpcl_1_1_decision_forest_trainer.html#aa277da116e3e9eab9f6e95b11257b7d4">更多...</a><br /></td></tr>
<tr class="separator:aa277da116e3e9eab9f6e95b11257b7d4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abc5a1198a387ef416195c9a953e6138a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#abc5a1198a387ef416195c9a953e6138a">train</a> (<a class="el" href="classpcl_1_1_decision_forest.html">DecisionForest</a>&lt; NodeType &gt; &amp;forest)</td></tr>
<tr class="memdesc:abc5a1198a387ef416195c9a953e6138a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Trains a decision forest using the set training data and settings.  <a href="classpcl_1_1_decision_forest_trainer.html#abc5a1198a387ef416195c9a953e6138a">更多...</a><br /></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a93cf799c3515a9541f7bf3ab667797db"><td class="memItemLeft" align="right" valign="top"><a id="a93cf799c3515a9541f7bf3ab667797db"></a>
size_t&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#a93cf799c3515a9541f7bf3ab667797db">num_of_trees_to_train_</a></td></tr>
<tr class="memdesc:a93cf799c3515a9541f7bf3ab667797db"><td class="mdescLeft">&#160;</td><td class="mdescRight">The number of trees to train. <br /></td></tr>
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<tr class="memitem:ab4986bec3c107133aab42a7f8a54849f"><td class="memItemLeft" align="right" valign="top"><a id="ab4986bec3c107133aab42a7f8a54849f"></a>
<a class="el" href="classpcl_1_1_decision_tree_trainer.html">pcl::DecisionTreeTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a></td></tr>
<tr class="memdesc:ab4986bec3c107133aab42a7f8a54849f"><td class="mdescLeft">&#160;</td><td class="mdescRight">The trainer for the decision trees of the forest. <br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;class FeatureType, class DataSet, class LabelType, class ExampleIndex, class NodeType&gt;<br />
class pcl::DecisionForestTrainer&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;</h3>

<p>Trainer for decision trees. </p>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="a5576065b2e1de3fc70a03822665437c4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a5576065b2e1de3fc70a03822665437c4">&#9670;&nbsp;</a></span>setDecisionTreeDataProvider()</h2>

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<div class="memtemplate">
template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setDecisionTreeDataProvider </td>
          <td>(</td>
          <td class="paramtype">boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_decision_tree_trainer_data_provider.html">pcl::DecisionTreeTrainerDataProvider</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt; &gt; &amp;&#160;</td>
          <td class="paramname"><em>dtdp</em></td><td>)</td>
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<p>Specify the data provider. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">dtdp</td><td>The data provider that should implement getDatasetAndLabels(...) function </td></tr>
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  </dd>
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<div class="fragment"><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;      {</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setDecisionTreeDataProvider(dtdp);</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_decision_forest_trainer_html_ab4986bec3c107133aab42a7f8a54849f"><div class="ttname"><a href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">pcl::DecisionForestTrainer::decision_tree_trainer_</a></div><div class="ttdeci">pcl::DecisionTreeTrainer&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt; decision_tree_trainer_</div><div class="ttdoc">The trainer for the decision trees of the forest.</div><div class="ttdef"><b>Definition:</b> decision_forest_trainer.h:201</div></div>
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<a id="ab3e0194e7f6c31d26903d963c26bcbd0"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ab3e0194e7f6c31d26903d963c26bcbd0">&#9670;&nbsp;</a></span>setExamples()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setExamples </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>examples</em></td><td>)</td>
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<p>Example indices that specify the data used for training. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">examples</td><td>The examples. </td></tr>
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  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;      {</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setExamples (examples);</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#affb20f4c78f522abf7a6e318ca999958">&#9670;&nbsp;</a></span>setFeatureHandler()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setFeatureHandler </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a>&lt; FeatureType, DataSet, ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>feature_handler</em></td><td>)</td>
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<p>Sets the feature handler used to create and evaluate features. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">feature_handler</td><td>The feature handler. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;      {</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setFeatureHandler (feature_handler);</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#af73b24f6125f364b9002da7f31d272b7">&#9670;&nbsp;</a></span>setLabelData()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setLabelData </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; LabelType &gt; &amp;&#160;</td>
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<p>Sets the label data corresponding to the example data. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">label_data</td><td>The label data. </td></tr>
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  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      {</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setLabelData (label_data);</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ac9fb156d93023ae28d2868cc77f9f232">&#9670;&nbsp;</a></span>setMaxTreeDepth()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setMaxTreeDepth </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>max_tree_depth</em></td><td>)</td>
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<p>Sets the maximum depth of the learned tree. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">max_tree_depth</td><td>Maximum depth of the learned tree. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      {</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setMaxTreeDepth (max_tree_depth);</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#abe3c4a349ff797233b7588b144072380">&#9670;&nbsp;</a></span>setMinExamplesForSplit()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setMinExamplesForSplit </td>
          <td>(</td>
          <td class="paramtype">size_t&#160;</td>
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<p>Sets the minimum number of examples to continue growing a tree. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">n</td><td>Number of examples </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;      {</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setMinExamplesForSplit(n);</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a694f64f2fc578961105cb2de053c8ebe">&#9670;&nbsp;</a></span>setNumberOfTreesToTrain()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setNumberOfTreesToTrain </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>num_of_trees</em></td><td>)</td>
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<p>Sets the number of trees to train. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">num_of_trees</td><td>The number of trees. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      {</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#a93cf799c3515a9541f7bf3ab667797db">num_of_trees_to_train_</a> = num_of_trees;</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_decision_forest_trainer_html_a93cf799c3515a9541f7bf3ab667797db"><div class="ttname"><a href="classpcl_1_1_decision_forest_trainer.html#a93cf799c3515a9541f7bf3ab667797db">pcl::DecisionForestTrainer::num_of_trees_to_train_</a></div><div class="ttdeci">size_t num_of_trees_to_train_</div><div class="ttdoc">The number of trees to train.</div><div class="ttdef"><b>Definition:</b> decision_forest_trainer.h:198</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#af157ebf3ab0fc36d970a56df889d0764">&#9670;&nbsp;</a></span>setNumOfFeatures()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setNumOfFeatures </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>num_of_features</em></td><td>)</td>
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<p>Sets the number of features used to find optimal decision features. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">num_of_features</td><td>The number of features. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;      {</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setNumOfFeatures (num_of_features);</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aaefbd227253039f1762c023363866977">&#9670;&nbsp;</a></span>setNumOfThresholds()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setNumOfThresholds </td>
          <td>(</td>
          <td class="paramtype">const size_t&#160;</td>
          <td class="paramname"><em>num_of_threshold</em></td><td>)</td>
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<p>Sets the number of thresholds tested for finding the optimal decision threshold on the feature responses. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">num_of_threshold</td><td>The number of thresholds. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      {</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setNumOfThresholds (num_of_threshold);</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;      }</div>
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<a id="aa277da116e3e9eab9f6e95b11257b7d4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa277da116e3e9eab9f6e95b11257b7d4">&#9670;&nbsp;</a></span>setRandomFeaturesAtSplitNode()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setRandomFeaturesAtSplitNode </td>
          <td>(</td>
          <td class="paramtype">bool&#160;</td>
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<p>Specify if the features are randomly generated at each split node. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">b</td><td>Do it or not. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      {</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setRandomFeaturesAtSplitNode(b);</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2d91edee4cd00765989daa0bb138dc16">&#9670;&nbsp;</a></span>setStatsEstimator()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setStatsEstimator </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt; LabelType, NodeType, DataSet, ExampleIndex &gt; &amp;&#160;</td>
          <td class="paramname"><em>stats_estimator</em></td><td>)</td>
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<p>Sets the object for estimating the statistics for tree nodes. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">stats_estimator</td><td>The statistics estimator. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;      {</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setStatsEstimator (stats_estimator);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a525a99f17519b3b66f022e4fb6522381">&#9670;&nbsp;</a></span>setThresholds()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setThresholds </td>
          <td>(</td>
          <td class="paramtype">std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>thres</em></td><td>)</td>
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<p>Specify the thresholds to be used when evaluating features. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">thres</td><td>The threshold values. </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;      {</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setThresholds(thres);</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;      }</div>
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<a id="a51bb668cde26ec215c738d6ea49321e0"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a51bb668cde26ec215c738d6ea49321e0">&#9670;&nbsp;</a></span>setTrainingDataSet()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::setTrainingDataSet </td>
          <td>(</td>
          <td class="paramtype">DataSet &amp;&#160;</td>
          <td class="paramname"><em>data_set</em></td><td>)</td>
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<p>Sets the input data set used for training. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">data_set</td><td>The data set used for training. </td></tr>
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<div class="fragment"><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;      {</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.setTrainingDataSet (data_set);</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#abc5a1198a387ef416195c9a953e6138a">&#9670;&nbsp;</a></span>train()</h2>

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template&lt;class FeatureType , class DataSet , class LabelType , class ExampleIndex , class NodeType &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_decision_forest_trainer.html">pcl::DecisionForestTrainer</a>&lt; FeatureType, DataSet, LabelType, ExampleIndex, NodeType &gt;::train </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_decision_forest.html">pcl::DecisionForest</a>&lt; NodeType &gt; &amp;&#160;</td>
          <td class="paramname"><em>forest</em></td><td>)</td>
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<p>Trains a decision forest using the set training data and settings. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">forest</td><td>Destination for the trained forest. </td></tr>
  </table>
  </dd>
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<div class="fragment"><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;{</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> tree_index = 0; tree_index &lt; <a class="code" href="classpcl_1_1_decision_forest_trainer.html#a93cf799c3515a9541f7bf3ab667797db">num_of_trees_to_train_</a>; ++tree_index)</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  {</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;    <a class="code" href="classpcl_1_1_decision_tree.html">pcl::DecisionTree&lt;NodeType&gt;</a> tree;</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;    <a class="code" href="classpcl_1_1_decision_forest_trainer.html#ab4986bec3c107133aab42a7f8a54849f">decision_tree_trainer_</a>.train (tree);</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160; </div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;    forest.push_back (tree);</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  }</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_decision_tree_html"><div class="ttname"><a href="classpcl_1_1_decision_tree.html">pcl::DecisionTree</a></div><div class="ttdoc">Class representing a decision tree.</div><div class="ttdef"><b>Definition:</b> decision_tree.h:52</div></div>
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<hr/>该类的文档由以下文件生成:<ul>
<li>ml/include/pcl/ml/dt/<a class="el" href="decision__forest__trainer_8h_source.html">decision_forest_trainer.h</a></li>
<li>ml/include/pcl/ml/impl/dt/<a class="el" href="decision__forest__trainer_8hpp_source.html">decision_forest_trainer.hpp</a></li>
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